Federated microburst detection

ABSTRACT

An example method includes a sensor detecting multiple packets of a flow during a specified total time period (e.g., a reporting time period). The total time period can be subdivided into multiple time periods. The sensor can analyze the detected packets to determine an amount of network utilization for each of the time periods. The sensor can then generate a flow summary based on the network utilization and the flow and send the flow summary to an analytics engine. Multiple other sensors can do similarly for their respective packets and flows. The analytics engine can receive the flow summaries from the various sensors and determine a correspondence between flow with high network utilization at a specific time period and a node or nodes. These nodes that experienced multiple flows with high network utilization for a certain period of time can be identified as experiencing a microburst.

TECHNICAL FIELD

The present technology pertains to network traffic monitoring and more specifically pertains to detecting microbursts within a network.

BACKGROUND

A microburst occurs when a network resource experiences a burst of traffic. A microburst can be difficult to detect and diagnose when it occurs on network components with excess capacity. For example, a port on a switch might experience 10% average utilization but have high packet loss because of microbursts.

A microburst can be caused by a single network event which triggers events on multiple network elements. For example, a command to a server might trigger a cascade of network operations that, momentarily, flood the network. Because these communications have relatively low byte-counts, they do not significantly impact overall network utilization, however they can temporarily overwhelm a network component such as a switch and cause packets to drop. For some applications, this behavior is unacceptable.

Detecting and diagnosing microbursts has generally been limited to detecting dropped packets and identifying buffer overflows on switches. These approaches can be useful at times, but fail to provide enough information to effectively detect and diagnose a microburst.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only example embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 shows an example network traffic monitoring system according to some example embodiments;

FIG. 2 illustrates an example network environment according to some example embodiments;

FIG. 3A shows an example map of nodes and flows on a network;

FIG. 3B shows an example representation of packet traffic at a node;

FIG. 4 shows an example method for detecting, summarizing, and analyzing packets for multiple flows associated with multiple sensors; and

FIGS. 5A and 5B illustrate example system embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

The present technology includes determining and diagnosing network traffic microbursts.

An example method includes a sensor detecting multiple packets of a flow during a specified total time period (e.g., a reporting time period). The total time period can be subdivided into multiple time periods. The sensor can analyze the detected packets to determine an amount of network utilization for each of the time periods. The sensor can then generate a flow summary based on the network utilization and various flow characteristics; the flow summary can then be sent to an analytics engine. Multiple other sensors can perform similar analysis for their respective packets and flows. The analytics engine can receive the flow summaries from the various sensors and determine a correspondence between flows with high network utilization at a specific time period; the analytics engine can further determine a correspondence between those flows and node(s). Nodes that experience multiple flows with high network utilization for a certain period of time can be identified as experiencing a microburst.

Description

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure.

The disclosed technology addresses the need in the art for determining and diagnosing network traffic microbursts in a data center.

A difficult problem for a network administrator to resolve can occur when a network generally has low utilization but still drops packets. The cause of these dropped packets can be a network traffic microburst. A microburst is a small period of high network utilization surrounded by periods of time of relatively low utilization. In some use cases that are highly intolerant of latency (e.g., high frequency stock trading, IPTV, TCP incast, etc.), microbursts can frustrate the purpose of the datacenter. Because microbursts occur during short periods of time, most traffic monitoring systems lack the granularity to adequately detect them. The dropped packets that result from microbursts can sometimes be misdiagnosed and not identified as traffic-related problems. Current solutions for microburst identification include having a switch indicate when a buffer for a port overflows and monitoring dropped packets. These approaches lack the specificity and granularity to adequately diagnose and correct microbursts.

Disclosed herein are approaches for detecting microbursts utilizing sensors deployed in a network traffic monitoring system. These sensors can detect flows at their source and send information about these flows to an analysis engine. This information can include a high degree of granularity and can be a summary of a reporting time period. The analysis engine can then use the sensor information to infer microbursts which occurred at various nodes throughout the datacenter. The analysis engine can also use an application dependency map to determine the likely cause of the microburst and determine possible remedies.

FIG. 1 shows an example network traffic monitoring system 100 according to some example embodiments. Network traffic monitoring system 100 can include configuration and image manager 102, sensors 104, external data sources 106, collectors 108, analytics module 110, policy engine 112, and presentation module 116. These modules may be implemented as hardware and/or software components. Although FIG. 1 illustrates an example configuration of the various components of network traffic monitoring system 100, those of skill in the art will understand that the components of network traffic monitoring system 100 or any system described herein can be configured in a number of different ways and can include any other type and number of components. For example, sensors 104 and collectors 108 can belong to one hardware and/or software module or multiple separate modules. Other modules can also be combined into fewer components and/or further divided into more components.

Configuration and image manager 102 can provision and maintain sensors 104. In some example embodiments, sensors 104 can reside within virtual machine images, and configuration and image manager 102 can be the component that also provisions virtual machine images.

Configuration and image manager 102 can configure and manage sensors 104. When a new virtual machine is instantiated or when an existing one is migrated, configuration and image manager 102 can provision and configure a new sensor on the machine. In some example embodiments configuration and image manager 102 can monitor the health of sensors 104. For instance, configuration and image manager 102 may request status updates or initiate tests. In some example embodiments, configuration and image manager 102 can also manage and provision virtual machines.

In some example embodiments, configuration and image manager 102 can verify and validate sensors 104. For example, sensors 104 can be provisioned a unique ID that is created using a one-way hash function of its basic input/output system (BIOS) universally unique identifier (UUID) and a secret key stored on configuration and image manager 102. This UUID can be a large number that is difficult for an imposter sensor to guess. In some example embodiments, configuration and image manager 102 can keep sensors 104 up to date by installing new versions of their software and applying patches. Configuration and image manager 102 can obtain these updates automatically from a local source or the Internet.

Sensors 104 can reside on nodes of a data center network (e.g., virtual partition, hypervisor, physical server, switch, router, gateway, other network device, other electronic device, etc.). In general, a virtual partition may be an instance of a virtual machine (VM) (e.g., VM 104 a), sandbox, container (e.g., container 104 c), or any other isolated environment that can have software operating within it. The software may include an operating system and application software. For software running within a virtual partition, the virtual partition may appear to be a distinct physical server. In some example embodiments, a hypervisor (e.g., hypervisor 104 b) may be a native or “bare metal” hypervisor that runs directly on hardware, but that may alternatively run under host software executing on hardware. Sensors 104 can monitor communications to and from the nodes and report on environmental data related to the nodes (e.g., node IDs, statuses, etc.). Sensors 104 can send their records over a high-speed connection to collectors 108 for storage. Sensors 104 can comprise a piece of software (e.g., running on a VM, container, virtual switch, hypervisor, physical server, or other device), an application-specific integrated circuit (ASIC) (e.g., a component of a switch, gateway, router, standalone packet monitor, or other network device including a packet capture (PCAP) module or similar technology), or an independent unit (e.g., a device connected to a network device's monitoring port or a device connected in series along a main trunk of a datacenter). It should be understood that various software and hardware configurations can be used as sensors 104. Sensors 104 can be lightweight, thereby minimally impeding normal traffic and compute resources in a datacenter. Sensors 104 can “sniff” packets being sent over its host network interface card (NIC) or individual processes can be configured to report traffic to sensors 104. This sensor structure allows for robust capture of granular (i.e., specific) network traffic data from each hop of data transmission.

As sensors 104 capture communications, they can continuously send network traffic data to collectors 108. The network traffic data can relate to a packet, a collection of packets, a flow, a group of flows, etc. The network traffic data can also include other details such as the VM BIOS ID, sensor ID, associated process ID, associated process name, process user name, sensor private key, geo-location of a sensor, environmental details, etc. The network traffic data can include information describing the communication on all layers of the Open Systems Interconnection (OSI) model. For example, the network traffic data can include signal strength (if applicable), source/destination media access control (MAC) address, source/destination internet protocol (IP) address, protocol, port number, encryption data, requesting process, a sample packet, etc.

In some example embodiments, sensors 104 can preprocess network traffic data before sending to collectors 108. For example, sensors 104 can remove extraneous or duplicative data or they can create a summary of the data (e.g., latency, packets and bytes sent per flow, flagged abnormal activity, etc.). In some example embodiments, sensors 104 can be configured to only capture certain types of connection information and disregard the rest. Because it can be overwhelming for a system to capture every packet in a network, in some example embodiments, sensors 104 can be configured to capture only a representative sample of packets (e.g., every 1,000th packet or other suitable sample rate).

Sensors 104 can send network traffic data to one or multiple collectors 108. In some example embodiments, sensors 104 can be assigned to a primary collector and a secondary collector. In other example embodiments, sensors 104 are not assigned a collector, but can determine an optimal collector through a discovery process. Sensors 104 can change where they send their network traffic data if their environments change, such as if a certain collector experiences failure or if a sensor is migrated to a new location and becomes closer to a different collector. In some example embodiments, sensors 104 can send different types of network traffic data to different collectors. For example, sensors 104 can send network traffic data related to one type of process to one collector and network traffic data related to another type of process to another collector.

Collectors 108 can serve as a repository for the data recorded by sensors 104. In some example embodiments, collectors 108 can be directly connected to a top of rack switch. In other example embodiments, collectors 108 can be located near an end of row switch. Collectors 108 can be located on or off premises. It will be appreciated that the placement of collectors 108 can be optimized according to various priorities such as network capacity, cost, and system responsiveness. In some example embodiments, data storage of collectors 108 is located in an in-memory database, such as dashDB by International Business Machines. This approach benefits from rapid random access speeds that typically are required for analytics software. Alternatively, collectors 108 can utilize solid state drives, disk drives, magnetic tape drives, or a combination of the foregoing according to cost, responsiveness, and size requirements. Collectors 108 can utilize various database structures such as a normalized relational database or NoSQL database.

In some example embodiments, collectors 108 may only serve as network storage for network traffic monitoring system 100. In other example embodiments, collectors 108 can organize, summarize, and preprocess data. For example, collectors 108 can tabulate how often packets of certain sizes or types are transmitted from different nodes of a data center. Collectors 108 can also characterize the traffic flows going to and from various nodes. In some example embodiments, collectors 108 can match packets based on sequence numbers, thus identifying traffic flows and connection links. In some example embodiments, collectors 108 can flag anomalous data. Because it would be inefficient to retain all data indefinitely, in some example embodiments, collectors 108 can periodically replace detailed network traffic flow data with consolidated summaries. In this manner, collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data of other periods of time (e.g., day, week, month, year, etc.). By organizing, summarizing, and preprocessing the network traffic flow data, collectors 108 can help network traffic monitoring system 100 scale efficiently. Although collectors 108 are generally referred to herein in the plurality, it will be appreciated that collectors 108 can be implemented using a single machine, especially for smaller datacenters.

In some example embodiments, collectors 108 can receive data from external data sources 106, such as security reports, white-lists (106 a), IP watchlists (106 b), whois data (106 c), or out-of-band data, such as power status, temperature readings, etc.

In some example embodiments, network traffic monitoring system 100 can include a wide bandwidth connection between collectors 108 and analytics module 110. Analytics module 110 can include application dependency (ADM) module 160, reputation module 162, vulnerability module 164, malware detection module 166, etc., to accomplish various tasks with respect to the flow data collected by sensors 104 and stored in collectors 108. In some example embodiments, network traffic monitoring system 100 can automatically determine network topology. Using network traffic flow data captured by sensors 104, network traffic monitoring system 100 can determine the type of devices existing in the network (e.g., brand and model of switches, gateways, machines, etc.), physical locations (e.g., latitude and longitude, building, datacenter, room, row, rack, machine, etc.), interconnection type (e.g., 10 Gb Ethernet, fiber-optic, etc.), and network characteristics (e.g., bandwidth, latency, etc.). Automatically determining the network topology can assist with integration of network traffic monitoring system 100 within an already established datacenter. Furthermore, analytics module 110 can detect changes of network topology without the need of further configuration.

Analytics module 110 can determine dependencies of components within the network using ADM module 160. For example, if component A routinely sends data to component B but component B never sends data to component A, then analytics module 110 can determine that component B is dependent on component A, but A is likely not dependent on component B. If, however, component B also sends data to component A, then they are likely interdependent. These components can be processes, virtual machines, hypervisors, virtual local area networks (VLANs), etc. Once analytics module 110 has determined component dependencies, it can then form a component (“application”) dependency map. This map can be instructive when analytics module 110 attempts to determine a root cause of a failure (because failure of one component can cascade and cause failure of its dependent components). This map can also assist analytics module 110 when attempting to predict what will happen if a component is taken offline. Additionally, analytics module 110 can associate edges of an application dependency map with expected latency, bandwidth, etc. for that individual edge.

Analytics module 110 can establish patterns and norms for component behavior. For example, it can determine that certain processes (when functioning normally) will only send a certain amount of traffic to a certain VM using a small set of ports. Analytics module can establish these norms by analyzing individual components or by analyzing data coming from similar components (e.g., VMs with similar configurations). Similarly, analytics module 110 can determine expectations for network operations. For example, it can determine the expected latency between two components, the expected throughput of a component, response times of a component, typical packet sizes, traffic flow signatures, etc. In some example embodiments, analytics module 110 can combine its dependency map with pattern analysis to create reaction expectations. For example, if traffic increases with one component, other components may predictably increase traffic in response (or latency, compute time, etc.).

In some example embodiments, analytics module 110 can use machine learning techniques to identify security threats to a network using malware detection module 166. For example, malware detection module 166 can be provided with examples of network states corresponding to an attack and network states corresponding to normal operation. Malware detection module 166 can then analyze network traffic flow data to recognize when the network is under attack. In some example embodiments, the network can operate within a trusted environment for a time so that analytics module 110 can establish baseline normalcy. In some example embodiments, analytics module 110 can contain a database of norms and expectations for various components. This database can incorporate data from sources external to the network (e.g., external sources 106). Analytics module 110 can then create access policies for how components can interact using policy engine 112. In some example embodiments, policies can be established external to network traffic monitoring system 100 and policy engine 112 can detect the policies and incorporate them into analytics module 110. A network administrator can manually tweak the policies. Policies can dynamically change and be conditional on events. These policies can be enforced by the components depending on a network control scheme implemented by a network. Policy engine 112 can maintain these policies and receive user input to change the policies.

Policy engine 112 can configure analytics module 110 to establish or maintain network policies. For example, policy engine 112 may specify that certain machines should not intercommunicate or that certain ports are restricted. A network and security policy controller (not shown) can set the parameters of policy engine 112. In some example embodiments, policy engine 112 can be accessible via presentation module 116. In some example embodiments, policy engine 112 can include policy data 112. In some example embodiments, policy data 112 can include endpoint group (EPG) data 114, which can include the mapping of EPGs to IP addresses and/or MAC addresses. In some example embodiments, policy data 112 can include policies for handling data packets.

In some example embodiments, analytics module 110 can simulate changes in the network. For example, analytics module 110 can simulate what may result if a machine is taken offline, if a connection is severed, or if a new policy is implemented. This type of simulation can provide a network administrator with greater information on what policies to implement. In some example embodiments, the simulation may serve as a feedback loop for policies. For example, there can be a policy that if certain policies would affect certain services (as predicted by the simulation) those policies should not be implemented. Analytics module 110 can use simulations to discover vulnerabilities in the datacenter. In some example embodiments, analytics module 110 can determine which services and components will be affected by a change in policy. Analytics module 110 can then take necessary actions to prepare those services and components for the change. For example, it can send a notification to administrators of those services and components, it can initiate a migration of the components, it can shut the components down, etc.

In some example embodiments, analytics module 110 can supplement its analysis by initiating synthetic traffic flows and synthetic attacks on the datacenter. These artificial actions can assist analytics module 110 in gathering data to enhance its model. In some example embodiments, these synthetic flows and synthetic attacks are used to verify the integrity of sensors 104, collectors 108, and analytics module 110. Over time, components may occasionally exhibit anomalous behavior. Analytics module 110 can analyze the frequency and severity of the anomalous behavior to determine a reputation score for the component using reputation module 162. Analytics module 110 can use the reputation score of a component to selectively enforce policies. For example, if a component has a high reputation score, the component may be assigned a more permissive policy or more permissive policies; while if the component frequently violates (or attempts to violate) its relevant policy or policies, its reputation score may be lowered and the component may be subject to a stricter policy or stricter policies. Reputation module 162 can correlate observed reputation score with characteristics of a component. For example, a particular virtual machine with a particular configuration may be more prone to misconfiguration and receive a lower reputation score. When a new component is placed in the network, analytics module 110 can assign a starting reputation score similar to the scores of similarly configured components. The expected reputation score for a given component configuration can be sourced outside of the datacenter. A network administrator can be presented with expected reputation scores for various components before installation, thus assisting the network administrator in choosing components and configurations that will result in high reputation scores.

Some anomalous behavior can be indicative of a misconfigured component or a malicious attack. Certain attacks may be easy to detect if they originate outside of the datacenter, but can prove difficult to detect and isolate if they originate from within the datacenter. One such attack could be a distributed denial of service (DDOS) where a component or group of components attempt to overwhelm another component with spurious transmissions and requests. Detecting an attack or other anomalous network traffic can be accomplished by comparing the expected network conditions with actual network conditions. For example, if a traffic flow varies from its historical signature (packet size, transport control protocol header options, etc.) it may be an attack.

In some cases, a traffic flow may be expected to be reported by a sensor, but the sensor may fail to report it. This situation could be an indication that the sensor has failed or become compromised. By comparing the network traffic flow data from multiple sensors 104 spread throughout the datacenter, analytics module 110 can determine if a certain sensor is failing to report a particular traffic flow.

Presentation module 116 can include serving layer 118, authentication module 120, web front end 122, public alert module 124, and third party tools 126. In some example embodiments, presentation module 116 can provide an external interface for network monitoring system 100. Using presentation module 116, a network administrator, external software, etc. can receive data pertaining to network monitoring system 100 via a webpage, application programming interface (API), audiovisual queues, etc. In some example embodiments, presentation module 116 can preprocess and/or summarize data for external presentation. In some example embodiments, presentation module 116 can generate a webpage. As analytics module 110 processes network traffic flow data and generates analytic data, the analytic data may not be in a human-readable form or it may be too large for an administrator to navigate. Presentation module 116 can take the analytic data generated by analytics module 110 and further summarize, filter, and organize the analytic data as well as create intuitive presentations of the analytic data.

Serving layer 118 can be the interface between presentation module 116 and analytics module 110. As analytics module 110 generates reports, predictions, and conclusions, serving layer 118 can summarize, filter, and organize the information that comes from analytics module 110. In some example embodiments, serving layer 118 can also request raw data from a sensor or collector.

Web frontend 122 can connect with serving layer 118 to present the data from serving layer 118 in a webpage. For example, web frontend 122 can present the data in bar charts, core charts, tree maps, acyclic dependency maps, line graphs, tables, etc. Web frontend 122 can be configured to allow a user to “drill down” on information sets to get a filtered data representation specific to the item the user wishes to drill down to. For example, individual traffic flows, components, etc. Web frontend 122 can also be configured to allow a user to filter by search. This search filter can use natural language processing to analyze the user's input. There can be options to view data relative to the current second, minute, hour, day, etc. Web frontend 122 can allow a network administrator to view traffic flows, application dependency maps, network topology, etc.

In some example embodiments, web frontend 122 may be solely configured to present information. In other example embodiments, web frontend 122 can receive inputs from a network administrator to configure network traffic monitoring system 100 or components of the datacenter. These instructions can be passed through serving layer 118 to be sent to configuration and image manager 102 or policy engine 112. Authentication module 120 can verify the identity and privileges of users. In some example embodiments, authentication module 120 can grant network administrators different rights from other users according to established policies.

Public alert module 124 can identify network conditions that satisfy specified criteria and push alerts to third party tools 126. Public alert module 124 can use analytic data generated or accessible through analytics module 110. One example of third party tools 126 is a security information and event management system (SIEM). Third party tools 126 may retrieve information from serving layer 118 through an API and present the information according to the SIEM's user interfaces.

FIG. 2 illustrates an example network environment 200 according to some example embodiments. It should be understood that, for the network environment 100 and any environment discussed herein, there can be additional or fewer nodes, devices, links, networks, or components in similar or alternative configurations. Example embodiments with different numbers and/or types of clients, networks, nodes, cloud components, servers, software components, devices, virtual or physical resources, configurations, topologies, services, appliances, deployments, or network devices are also contemplated herein. Further, network environment 200 can include any number or type of resources, which can be accessed and utilized by clients or tenants. The illustrations and examples provided herein are for clarity and simplicity.

Network environment 200 can include network fabric 212, layer 2 (L2) network 206, layer 3 (L3) network 208, endpoints 210 a, 210 b, . . . , and 210 d (collectively, “204”). Network fabric 212 can include spine switches 202 a, 202 b, . . . , 202 n (collectively, “202”) connected to leaf switches 204 a, 204 b, 204 c, . . . , 204 n (collectively, “204”). Spine switches 202 can connect to leaf switches 204 in network fabric 212. Leaf switches 204 can include access ports (or non-fabric ports) and fabric ports. Fabric ports can provide uplinks to spine switches 202, while access ports can provide connectivity for devices, hosts, endpoints, VMs, or other electronic devices (e.g., endpoints 204), internal networks (e.g., L2 network 206), or external networks (e.g., L3 network 208).

Leaf switches 204 can reside at the edge of network fabric 212, and can thus represent the physical network edge. In some cases, leaf switches 204 can be top-of-rack switches configured according to a top-of-rack architecture. In other cases, leaf switches 204 can be aggregation switches in any particular topology, such as end-of-row or middle-of-row topologies. Leaf switches 204 can also represent aggregation switches, for example.

Network connectivity in network fabric 212 can flow through leaf switches 204. Here, leaf switches 204 can provide servers, resources, VMs, or other electronic devices (e.g., endpoints 210), internal networks (e.g., L2 network 206), or external networks (e.g., L3 network 208), access to network fabric 212, and can connect leaf switches 204 to each other. In some example embodiments, leaf switches 204 can connect endpoint groups (EPGs) to network fabric 212, internal networks (e.g., L2 network 206), and/or any external networks (e.g., L3 network 208). EPGs can be used in network environment 200 for mapping applications to the network. In particular, EPGs can use a grouping of application endpoints in the network to apply connectivity and policy to the group of applications. EPGs can act as a container for buckets or collections of applications, or application components, and tiers for implementing forwarding and policy logic. EPGs also allow separation of network policy, security, and forwarding from addressing by instead using logical application boundaries. For example, each EPG can connect to network fabric 212 via leaf switches 204.

Endpoints 210 can connect to network fabric 212 via leaf switches 204. For example, endpoints 210 a and 210 b can connect directly to leaf switch 204 a, which can connect endpoints 210 a and 210 b to network fabric 212 and/or any other one of leaf switches 204. Endpoints 210 c and 210 d can connect to leaf switch 204 b via L2 network 206. Endpoints 210 c and 210 d and L2 network 206 are examples of LANs. LANs can connect nodes over dedicated private communications links located in the same general physical location, such as a building or campus.

Wide area network (WAN) 212 can connect to leaf switches 204 c or 204 d via L3 network 208. WANs can connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links. LANs and WANs can include layer 2 (L2) and/or layer 3 (L3) networks and endpoints.

The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol can refer to a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective size of each network. Endpoints 210 can include any communication device or component, such as a computer, server, hypervisor, virtual machine, container, process (e.g., running on a virtual machine), switch, router, gateway, host, device, external network, etc. In some example embodiments, endpoints 210 can include a server, hypervisor, process, or switch configured with virtual tunnel endpoint (VTEP) functionality which connects an overlay network with network fabric 212. The overlay network may allow virtual networks to be created and layered over a physical network infrastructure. Overlay network protocols, such as Virtual Extensible LAN (VXLAN), Network Virtualization using Generic Routing Encapsulation (NVGRE), Network Virtualization Overlays (NVO3), and Stateless Transport Tunneling (STT), can provide a traffic encapsulation scheme which allows network traffic to be carried across L2 and L3 networks over a logical tunnel. Such logical tunnels can be originated and terminated through VTEPs. The overlay network can host physical devices, such as servers, applications, endpoint groups, virtual segments, virtual workloads, etc. In addition, endpoints 210 can host virtual workload(s), clusters, and applications or services, which can connect with network fabric 212 or any other device or network, including an internal or external network. For example, endpoints 210 can host, or connect to, a cluster of load balancers or an EPG of various applications.

Network environment 200 can also integrate a network traffic monitoring system, such as the one shown in FIG. 1. For example, as shown in FIG. 2, the network traffic monitoring system can include sensors 104 a, 104 b, . . . , 104 n (collectively, “104”), collectors 108 a, 108 b, . . . 108 n (collectively, “108”), and analytics module 110. In some example embodiments, spine switches 202 do not have sensors 104. Analytics module 110 can receive and process network traffic data collected by collectors 108 and detected by sensors 104 placed on nodes located throughout network environment 200. In some example embodiments, analytics module 110 can be implemented in an active-standby model to ensure high availability, with a first analytics module functioning in a primary role and a second analytics module functioning in a secondary role. If the first analytics module fails, the second analytics module can take over control. Although analytics module 110 is shown to be a standalone network appliance in FIG. 2, it will be appreciated that analytics module 110 can also be implemented as a VM image that can be distributed onto a VM, a cluster of VMs, a software as a service (SaaS), or other suitable distribution model in various other example embodiments. In some example embodiments, sensors 104 can run on endpoints 210, leaf switches 204, spine switches 202, in-between network elements (e.g., sensor 104 h), etc. In some example embodiments, leaf switches 204 can each have an associated collector 108. For example, if leaf switch 204 is a top of rack switch then each rack can contain an assigned collector 108.

Although network fabric 212 is illustrated and described herein as an example leaf-spine architecture, one of ordinary skill in the art will readily recognize that the subject technology can be implemented based on any network topology, including any data center or cloud network fabric. Indeed, other architectures, designs, infrastructures, and variations are contemplated herein. For example, the principles disclosed herein are applicable to topologies including three-tier (including core, aggregation, and access levels), fat tree, mesh, bus, hub and spoke, etc. It should be understood that sensors and collectors can be placed throughout the network as appropriate according to various architectures.

FIG. 3A shows an example network topology 300 including nodes 302 a, 302 b, 302 k (collectively, “302”) and corresponding snapshots of network traffic data 304 a, 304 b, . . . 304 k (collectively, “302”) over a short period of time (e.g., a few microseconds). In FIG. 3A, source endpoints (e.g., physical servers, electronic devices, virtual machines, containers, etc.), such as nodes 302 a-302 f, can communicate within the network by sending data to destination endpoints, such as nodes 302 g-302 i, via network nodes (e.g., physical or virtual switches, routers, etc.), such as nodes 302 j or 302 k. For example, node 302 a can send data to node 302 j, and node 302 j can switch or route that data to be received by node 302 g. Nodes 302 a-302 i are not necessarily single endpoints and can each represent a server cluster, datacenter, or other grouping of (physical or virtual) computing devices. Nodes 302 may be identified by network address (e.g., IP address, MAC address, etc.), server name, host name, domain name, interface, port, and/or other suitable identifiers. Nodes 302 can each include a sensor (not shown), such as sensor 104 of FIG. 1, for monitoring packets sent to and received by the nodes.

In FIG. 3A, traffic can flow between nodes 302 as represented by the lines between the nodes. For example, one flow can be between nodes 302 _(a) and 302 _(g), using node 302 _(j) as an intermediary node. In general, a flow is a set of packets (including a null set or a solitary packet) sharing similar characteristics sent within a network over a certain time interval. For example, a flow can be a sequence of packets sent from a particular source to a particular unicast, anycast, or multicast destination. A flow can also comprise all packets in a specific transport connection or a media stream. However, a flow is not necessarily 1:1 mapped to a transport connection. A single node 302 can generate multiple flows and a single node 302 can receive multiple flows. Sensors on each node 302, such as sensors 104 of FIG. 1, can detect and describe flows in the datacenter.

Network traffic snapshots 304 can indicate how much traffic that a node 302 sends, receives, or otherwise experiences within a specified time interval. In FIG. 3A, nodes 302 a-302 c can generate flows which pass through node 302 j. Although each source node 302 a-302 c generally has plenty of excess capacity, the small spikes in traffic for nodes 302 a and 302 b combine and can temporarily overwhelm node 302 j. This can cause node 302 j to drop packets (e.g., if its packet buffer fills up) or otherwise experience negative effects of a microburst.

FIG. 3B shows an example of a more detailed network traffic snapshot 304. The snapshot 304 can describe multiple time periods 310 _(a)-310 _(c) (collectively, “310”) of an overall reporting period 312. As discussed, each time period can represent a number of milliseconds (e.g., 1, 2, 5, 10, 100), a number of seconds (e.g., 1, 2, 5, 10, 100), or any other division of time. In some embodiments, not all time periods are of the same duration; for example, each time period can be based on the total datacenter activity (e.g., when the datacenter is more active, the time periods can be shorter whereas when the datacenter is less active, the time periods can be longer), total datacenter network activity, etc. Limit threshold 314 can indicate a maximum number of packets that the respective node can process. In FIG. 3B, during time period 310 _(d), the node associated with snapshot 304 is shown experiencing traffic that is greater than what the node can process. This can cause the node to drop packets, delay processing packets, or otherwise negatively affect the behavior of the network.

As discussed elsewhere herein, a sensor running on each node 302 can continuously detect packets sent out from or received by the node. In some embodiments, the sensor can perform some pre-processing of network traffic data, such as by breaking up the network traffic data into reporting periods 312. For each reporting period 312, the sensor can aggregate the number of packets sent from/received by node 302, and send a summary to a collector (e.g., collector 108), an analytics module (e.g., analytics module 110), etc. A reporting period 312 can be a predetermined number of milliseconds, seconds, cycles, etc. In some embodiments, multiple sensors can provide summaries of network traffic data according to synchronized reporting periods; i.e., every reporting period 312 can begin at the same time. A reporting period 312 can include multiple time periods 310 which can be subdivisions of the reporting period 312.

The reporting period 312 duration, synchronization, and division into time periods 310 can be identical across all sensors 104. Alternatively, the reporting period 312 duration, synchronization, and division into time periods 310 can vary based on the type of sensor 104, the type of node 302, or the type of flow. For example, a high bandwidth flow can have small reporting period 312 durations (e.g., only a few milliseconds) while a low bandwidth flow can have large reporting period 312 durations (e.g., on the order of seconds, minutes, hours, etc.).

The summary of the reporting period can describe a time period 310 with the most network utilization. The summary can describe the number of packets sent during each of the multiple time periods 310. The summary can describe the excess network capacity for each of the multiple time periods 310. The summary can rank each of the multiple time periods 310 (e.g., a first time period 310 had the most network utilization, a second time period 310 had the second most network utilization, etc.). The summary can describe the associated flow (e.g., source and destination address, port number, associated application or service, etc.). The summary can include an indicator of how many packets were resent during the multiple time periods.

FIG. 4 shows an example method for detecting, summarizing, and analyzing packets for multiple flows associated with multiple sensors. First sensor 104 _(i) and second sensor 104 _(j) can detect and analyze respective packets during multiple time periods and send reports to flow analytics engine 403 (e.g., analytics module 110) which can determine if a microburst occurred. Although FIG. 4 is depicted using two sensors 104, any number of sensors 104 can be used for the purposes of this disclosure. Also, although the steps in FIG. 4 are similar across sensors 104, variations between steps are contemplated.

First sensor 104, can detect multiple packets of a flow over multiple periods of time (step 404). These multiple packets can be the packets that are detected at first sensor 104 _(i) while other multiple packets can be detected at second sensor 104 _(j). In some embodiments, step 404 includes analyzing a packet log. Step 404 can include incrementing a counter for each of the multiple periods of time. In some embodiments, step 404 is performed at a hardware level (e.g., by an ASIC, FPGA, etc.).

First sensor 104 _(i) can then determine an amount of network utilization for each period of time (step 406). The amount of network utilization can include a ranking of each period of time (e.g., period 5 had the highest utilization, 3 had the second highest, 4 the third, and so on). The amount of network utilization can include a count of packets, bytes, etc. The amount of network utilization can include a percentage or other description of the relative utilization. The amount of network utilization can include a Boolean determination that the network utilization was above a threshold amount.

The amount of network utilization can be an indication of the variance from the norm for that period of time. For example, first sensor 104, can determine the normal amount of network utilization (e.g., mean, median, mode, range, distribution, etc.) over the reporting period, multiple reporting periods, any number of past time periods, etc. First sensor 104 _(i) can then characterize the network usage for the relevant period of time in comparison to the normal amount.

The detection of packets (step 404) and the determination of the amount of network utilization (step 406) can occur simultaneously.

First sensor 104 _(i) can then identify a period of time with the greatest network utilization (step 408). In some embodiments, flow analysis engine 403 performs step 408 based on summaries received from first and second sensors 104 _(i) and 104 _(j). It should be understood that the greatest network utilization can be determined on a flow by flow basis. For example, each flow from a node 302 can have a different greatest network utilization; e.g., a first flow can have high utilization in period one while a second flow can have high utilization in period two.

In some embodiments, first sensor 104, detects the network usage for a first period of time and marks the first period of time as (as of that moment) having the greatest network utilization. When the second period of time begins, first sensor 104, can detect the network usage for the second period of time and, if the network usage is greater for the second period of time, the second period of time can be marked as having the greatest network utilization and the data for the first period of time can be discarded. This can continue through the multiple periods of time. Because only the current time period network utilization and the highest time period network utilization is maintained, such a technique can scale easily to applications where each reporting period has a high number of time periods. Alternatively, the network utilization for each time period can be preserved.

First sensor 104, can then generate a flow summary for the flow based on the multiple packets (step 410). The flow summary can include the network utilization for each of the multiple time periods. The flow summary can include a description of the flow (e.g., source and destination address, port, connections, cables, etc.).

Flow analysis engine 403 can receive flow summaries from the first and second sensors (step 412). For example, first sensor 104 _(i) and second sensor 104 _(j) can send flow summaries to collectors along with other flow reports. The collectors can then forward flow summaries to flow analysis engine 403.

Flow analysis engine 403 can then process the flow summaries from the first and second sensors and identify a microburst during the multiple periods of time (step 414).

Processing the flow summaries can include identifying a flow path for each flow. A flow path can include the nodes 302 that participate in the flow. For example, in FIG. 3A, a flow from node 302 _(c) to 302 _(h) can include node 302 _(c), node 302 _(k), and 302 _(h) in its flow path. The flow path can include locations, buildings, rooms, datacenters, systems, nodes 302, switches, routers, ports, interfaces, etc.

Flow analysis engine 403 can, using the flow summaries and associated flow paths, generate inferred utilization for nodes 302 in the network. For example, in FIG. 3A, flow analysis can generate an inferred utilization (indicated by representation 304 _(j)) based on flow summaries (indicated by representations 304 _(a), 304 _(b), and 304 _(c)). The inferred utilization can indicate the inferred packet count, byte count, number of active connections, inferred network utilization, number of flows that have abnormally high network utilization, etc.

Flow analysis engine 403 can then determine if a microburst occurred at node 302 based on the inferred utilization for the respective node 302. Determining that a microburst occurred can include determining if at any time period within the reporting period network utilization exceeded a predetermined threshold.

Flow analysis engine 403 can determine source nodes(s) 302 of the microburst. For example, if node 302 _(j) experiences a microburst, flow analysis engine can determine which flows contributed to the microburst. In other words, the flows from nodes 302 _(a) and 302 b experienced a burst that corresponded to the microburst at node 302 _(j) while node 302 _(c) did not experience a burst (even though node 302, did have some traffic during that time period). Flow analysis engine 403 can determine that the source node(s) 302 correspond to a similar process or application. For example, the flow summary can indicate a process name associated with the flow and flow analysis engine 403 can determine correspondence based on the name. The correspondence can also be based on a process argument, process identifier, parent process identifier, user name, or user identifier, etc.

Flow analysis engine 403 can determine that certain flows contributed to the microburst based on the flows going through the node 302 that experienced the microburst. In some embodiments, only those flows that experienced a burst in traffic can be determined to contribute to the microburst. Contributing flows can be marked as contributors to the microburst. Source node 302 for the contributing flows can be marked as contributors as well.

After determine source(s) of the microburst, flow analysis engine 403 can utilize an application dependency map (ADM) to determine an application, process, communication, etc. might have triggered the microburst. For example, a client device might make a query on a webserver and this query might cause a cascade of activity across a datacenter. Node 302 _(a) can be a data storage node while node 302 _(b) can be an identity node.

Flow analysis engine 403 can use the ADM to determine that the source node(s) 302 of the microburst are within the same region, network, cluster, system, etc. For example, if node 302 _(j) is a perimeter node 302 for a cluster; traffic coming from the source nodes 302 might be forced to go through the perimeter node 302 that experienced the microburst. A network administrator or an automated network configuration or network management system can then move one of the nodes 302 outside of the cluster or add capacity as the perimeter of the cluster to avoid future microbursts.

The information from the ADM, the identified source(s) of the microburst, the identified trigger of the microburst, etc. can be used to establish routes in a network to avoid microbursts. For example, flows from node 302 _(b) to node 302 _(h) can be routed through node 302 _(k) to avoid the collisions that otherwise might occur at node 302 _(j). The network administrator, network configuration system, network management system, or other suitable entity can direct node 302 _(b) accordingly. In some embodiments, the ADM and the identified source(s) can inform a modification of a placement of a wire or connection; for example, a connection can be moved to a different switch, interface, VLAN, etc. on a swtich.

The ADM, identified source(s), and identified trigger(s) can be used to prioritize flows. For example, one source node 302 might trigger flow that serves as a response to a query and another flow that is for backup or logging purposes. The backup or logging flow can be delayed or otherwise deprioritized so that the response flow does not get delayed.

It should be understood that although the principles disclosed herein are generally within the context of network utilization, these principles can be applied to other types of resources. For example, hard disk usage, power consumption, processer utilization, etc. can all experience microbursts. Sensors 104 and flow analytics engine 403 can detect the resource utilization and identify these microbursts.

In some embodiments, first sensor 104, can monitor multiple flows and send multiple flow summaries to flow analysis engine 403.

FIG. 5A and FIG. 5B illustrate example system embodiments. The more appropriate embodiment will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system embodiments are possible.

FIG. 5A illustrates a conventional system bus computing system architecture 500 wherein the components of the system are in electrical communication with each other using a bus 505. Example system 500 includes a processing unit (CPU or processor) 510 and a system bus 505 that couples various system components including the system memory 515, such as read only memory (ROM) 570 and random access memory (RAM) 575, to the processor 510. The system 500 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 510. The system 500 can copy data from the memory 515 and/or the storage device 530 to the cache 512 for quick access by the processor 510. In this way, the cache can provide a performance boost that avoids processor 510 delays while waiting for data. These and other modules can control or be configured to control the processor 510 to perform various actions. Other system memory 515 may be available for use as well. The memory 515 can include multiple different types of memory with different performance characteristics. The processor 510 can include any general purpose processor and a hardware module or software module, such as module 1 537, module 7 534, and module 3 536 stored in storage device 530, configured to control the processor 910 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 510 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction with the computing device 500, an input device 545 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 535 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 500. The communications interface 540 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 530 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 575, read only memory (ROM) 570, and hybrids thereof.

The storage device 530 can include software modules 537, 534, 536 for controlling the processor 510. Other hardware or software modules are contemplated. The storage device 530 can be connected to the system bus 505. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 510, bus 505, display 535, and so forth, to carry out the function.

FIG. 5B illustrates an example computer system 550 having a chipset architecture that can be used in executing the described method and generating and displaying a graphical user interface (GUI). Computer system 550 is an example of computer hardware, software, and firmware that can be used to implement the disclosed technology. System 550 can include a processor 555, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 555 can communicate with a chipset 560 that can control input to and output from processor 555. In this example, chipset 560 outputs information to output 565, such as a display, and can read and write information to storage device 570, which can include magnetic media, and solid state media, for example. Chipset 560 can also read data from and write data to RAM 575. A bridge 580 for interfacing with a variety of user interface components 585 can be provided for interfacing with chipset 560. Such user interface components 585 can include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to system 550 can come from any of a variety of sources, machine generated and/or human generated.

Chipset 560 can also interface with one or more communication interfaces 590 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein can include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 555 analyzing data stored in storage 570 or 575. Further, the machine can receive inputs from a user via user interface components 585 and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 555.

It can be appreciated that example systems 500 and 550 can have more than one processor 510 or be part of a group or cluster of computing devices networked together to provide greater processing capability.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. Moreover, claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim. 

1. A computer-implemented method comprising: receiving, by a network device, within a specified total time period, a first set of packets associated with a first flow; analyzing, by a sensor associated with the network device, the first set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determining, by the sensor, a first incremental time period of the specified total time period associated with a maximum amount of network utilization; and generating first flow summary data including an indication that the first incremental time period is associated with the maximum amount of network utilization and a first amount of network utilization associated with the first incremental time period.
 2. The computer-implemented method of claim 1, further comprising: receiving, by the network device, within the specified total time period, a second set of packets associated with a second flow; analyzing, by the sensor, the second set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determining, by the sensor, a second incremental time period of the specified total time period associated with a maximum amount of network utilization; generating second flow summary data including an indication that the second incremental period is associated with the maximum amount of network utilization and a second amount of the network utilization associated with the second incremental period; receiving the first flow summary data and the second flow summary data; and determining that the first incremental time period and the second incremental period are a same time period.
 3. The computer-implemented method of claim 2, further comprising: determining an application dependency map; and based on the application dependency map, determining a new route for the second flow so that the second flow does not pass through a same port of the network device or the network device.
 4. The computer-implemented method of claim 1, further comprising: determining that a first endpoint associated with the first flow and a second endpoint associated with the second flow are part of a same cluster based at least in part on one or more correspondences between the first flow and the second flow.
 5. The computer-implemented method of claim 4, further comprising: identifying a first process of the first endpoint; and identifying a second process of the second endpoint, wherein the one or more correspondences between the first flow and the second flow include the first process and the second process having at least one of a same process name, process argument, process identifier, parent process identifier, user name, or user identifier.
 6. The computer-implemented method of claim 1, further comprising: determining an application dependency map; and determining, based on the application dependency map, the first flow summary data, the second flow summary a source endpoint associated with the first flow and the second flow.
 7. The computer-implemented method of claim 1, wherein the sensor is implemented within an application-specific integrated circuit.
 8. A non-transitory computer-readable medium comprising instructions that, when executed by a processor of a computing system, cause the computing system to: receive, by a network device, within a specified total time period, a first set of packets associated with a first flow; analyze, by a sensor associated with the network device, the first set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determine, by the sensor, a first incremental time period of the specified total time period associated with a maximum amount of network utilization; and generate first flow summary data including an indication that the first incremental time period is associated with the maximum amount of network utilization and a first amount of network utilization associated with the first incremental time period.
 9. The non-transitory computer-readable medium of claim 8, wherein the instructions when executed further cause the computing system to: receive, by the network device, within the specified total time period, a second set of packets associated with a second flow; analyze, by the sensor, the second set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determine, by the sensor, a second incremental time period of the specified total time period associated with a maximum amount of network utilization; generate second flow summary data including an indication that the second incremental period is associated with the maximum amount of network utilization and a second amount of the network utilization associated with the second incremental period; receive the first flow summary data and the second flow summary data; and determine that the first incremental time period and the second incremental period are a same time period.
 10. The non-transitory computer-readable medium of claim 9, wherein the instructions when executed further cause the computing system to: determine an application dependency map; and based on the application dependency map, determine a new route for the second flow so that the second flow does not pass through a same port of the network device or the network device.
 11. The non-transitory computer-readable medium of claim 8, wherein the instructions when executed further cause the computing system to: determine that the first network device and the second network device are part of a same cluster based at least in part on one or more correspondences between the first flow and the second flow.
 12. The non-transitory computer-readable medium of claim 11, wherein the instructions when executed further cause the computing system to: identify a first process of the first network device corresponding to the first flow; and identify a second process of the second network device corresponding to the second flow, wherein the one or more correspondences between the first flow and the second flow include the first process and the second process having at least one of a same process name, process argument, process identifier, parent process identifier, user name, or user identifier.
 13. The non-transitory computer-readable medium of claim 8, wherein the instructions when executed further cause the computing system to: determine an application dependency map; and determine, based on the application dependency map and the flow summary, that a trigger network device caused at least part of the network utilization.
 14. The non-transitory computer-readable medium of claim 8, wherein the first sensor is implemented within an application-specific integrated circuit.
 15. A system comprising: a processor; and a memory device including instructions stored that, when executed by the processor, cause the system to: receive, by a network device, within a specified total time period, a first set of packets associated with a first flow; analyze, by a sensor associated with the network device, the first set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determine, by the sensor, a first incremental time period of the specified total time period associated with a maximum amount of network utilization; and generate first flow summary data including an indication that the first incremental time period is associated with the maximum amount of network utilization and a first amount of network utilization associated with the first incremental time period.
 16. The system of claim 15, wherein the instructions, when executed by the processor, further cause the system to: receive, by the network device, within the specified total time period, a second set of packets associated with a second flow; analyze, by the sensor, the second set of packets to determine an amount of network utilization for each incremental time period of the specified total time period; determine, by the sensor, a second incremental time period of the specified total time period associated with a maximum amount of network utilization; generate second flow summary data including an indication that the second incremental period is associated with the maximum amount of network utilization and a second amount of the network utilization associated with the second incremental period; receive the first flow summary data and the second flow summary data; and determine that the first incremental time period and the second incremental period are a same time period.
 17. The system of claim 16, wherein the instructions, when executed by the processor, further cause the system to: determine an application dependency map; and based on the application dependency map, determine a new route for the second flow so that the second flow does not pass through a same port of the network device or the network device.
 18. The system of claim 15, wherein the instructions, when executed by the processor, further cause the system to: determine that the first network device and the second network device are part of a same cluster based at least in part on one or more correspondences between the first flow and the second flow.
 19. The system of claim 18, wherein the instructions, when executed by the processor, further cause the system to: identify a first process of the first network device corresponding to the first flow; and identify a second process of the second network device corresponding to the second flow, wherein the one or more correspondences between the first flow and the second flow include the first process and the second process having at least one of a same process name, process argument, process identifier, parent process identifier, user name, or user identifier.
 20. The system of claim 15, wherein the instructions, when executed by the processor, further cause the system to: determine a first destination device of a first flow that corresponds to the maximum amount of network utilization; determine a second destination device of a second flow that corresponds to the maximum amount of network utilization; and determine that the first destination device and the second destination device are part of a same cluster based on the first flow and the second flow corresponding to the maximum amount of network utilization. 